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Personalization via Friendsourcing

Personalization via Friendsourcing. Michael Bernstein, MIT Computer Science and Artificial Intelligence Lab.

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Personalization via Friendsourcing

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  1. Personalization via Friendsourcing Michael Bernstein, MIT Computer Science and Artificial Intelligence Lab Your friends maintain complex and accurate models of your interests, activities and personality.Can we design social applications to motivate them to share this knowledge to your benefit? FeedMe: Social Link Sharing Active users of RSS readers consume hundreds of news items each day, often passing interesting content to friends, family and colleagues. FeedMe is designed to facilitate this process and amplify it. FeedMe suggests friends who might be interested in each post. Recommendations are delivered via e-mail. What impacts web content sharing? Online survey, N = 40 “A good friend of mine always knows what I'm interested in reading. He never sends me boring news articles or stupid YouTube links, and I love him for it.” Our data suggest that the more a person reads on the web, the more they share with others. (R2=.53, p<.001, N=100) Why share? 93% said they knew that the person would appreciate hearing about the content. Why not share? Many were unsure whether the link would be relevant enough, whether they had sent the recipient too many links recently, or it seemed like too much effort. When considering whether to share: “Will this link be interesting to this person? Will it be worth their time? Will it be delightful?” How do you go about finding and viewing new web content? How do you go about sharing web content? Recommend to: msbernst@mit.edu rcm@mit.edu meitros@gmail.com 1 today 0 today 0 today As feeds are forwarded to friends, FeedMe builds a vector-space representation of each recipient by mining the contents of the posts sent to them, and uses standard cosine-distance metrics to generate future recommendations. stat.us: status updates from the rest of us Collabio: People Tagging for Personalization Sociapedia: Friend-Authored Biographies Collabio is a tagging game embedded in Facebook. Players attempt to tag one of their friends by guessing the tags that others have used to describe that person. More points are awarded for agreeing with others. Collabio has gathered 7,780 unique tags on 3,831 individuals. The tags and tag counts can then be used to power personalized applications. In Collabio QnA (below) , users ask questions to identify friends and friends-of-friends who are likely to be able to answer. Advisors: Rob Miller and David Karger. Collaborators: Adam Marcus, Desney Tan, Greg Smith, Mary Czerwinski, Eric Horvitz.

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